A PRIMER ON SQL
3rd Edition
By Rahul Batra
A Primer on SQL
Third Edition
Rahul Batra
This book is for sale at http://leanpub.com/aprimeronsql
This version was published on 2015-02-25
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Also By Rahul Batra
A Primer on Java
To Mum and Dad
Contents
Preface i
About the author ii
Acknowledgements iii
1. An Introduction to SQL 1
1.1 SQL Commands Classification 1
1.2 Explaining Tables 2
2. Getting your database ready 4
2.1 Using Ingres 4
2.2 Using SQLite 4
2.3 Creating your own database 5
2.4 Table Creation 6
2.5 Inserting data 8
2.6 Writing your first query 8
3. Constraints 10
3.1 Selective fields INSERT 10
3.2 Primary Key Constraint 12
3.3 Unique Key Constraint 12
3.4 Differences between a Primary Key and a Unique Key 13
4. Operations on Tables 14
4.1 Dropping Tables 14
4.2 Creating new tables from existing tables 14
4.3 Modifying tables 15
4.4 Verifying the result in Ingres 16
4.5 Verifying the result in other DBMS’s 18
4.6 Showing table information in SQLite 18
5. Writing Basic Queries 19
5.1 Selecting a limited number of columns 19
5.2 Ordering the results 20
CONTENTS
5.3 Ordering using field abbreviations 20
5.4 Putting conditions with WHERE 21
5.5 Combining conditions 22
6. Manipulating Data 24
6.1 Inserting NULL’s 24
6.2 Inserting data into a table from another table 25
6.3 Updating existing data 25
6.4 Deleting data from tables 26
7. Organizing your data 28
7.1 Normalization 28
7.2 Atomicity 29
7.3 Repeating Groups 29
7.4 Splitting the table 30
8. Doing more with queries 33
8.1 Counting the records in a table 33
8.2 Column Aliases 34
8.3 Order of execution of SELECT queries 34
8.4 Using the LIKE operator 35
9. Calculated Fields 37
9.1 Mathematical calculations 37
9.2 String operations 38
9.3 Literal Values 39
10. Aggregation and Grouping 40
10.1 Aggregate Functions 40
10.2 Using DISTINCT with COUNT 40
10.3 Using MIN to find minimum values 41
10.4 Grouping Data 41
10.5 The HAVING Clause 43
11. Understanding Joins 44
11.1 What is a Join? 44
11.2 Alternative Join Syntax 45
11.3 Resolving ambiguity in join columns 46
11.4 Cross Joins 46
11.5 Self Joins 47
12. Subqueries 49
12.1 Types of subqueries 49
12.2 Using subqueries in INSERT statements 50
CONTENTS
Further Reading 52
Appendix: Major Database Management Systems 53
Glossary 54
Preface
Welcome to the third edition of A Primer on SQL. This edition features a new chapter on subqueries
and a few other changes suggested by readers. There is also some further information about using
this text with SQLite which has continued to soar new heights in popularity, while Ingres has seen
some slowness in recent times. However, this text remains database implementation agnostic.
I hope that old and new readers find this text even more useful now in its presentation. I have tried
to keep the spirit of the original text, a short introduction to the basics. As always, your questions,
comments, criticism, encouragement and corrections are most welcome and you can e-mail me at
rhlbatra[aht]hotmail[dot]com.
Rahul Batra (25th February 2015)
Preface to the first edition
Welcome to the first edition of A Primer on SQL. As you would be able to see, the book is fairly short
and is intended as an introduction to the basics of SQL. No prior experience with SQL is necessary,
but some knowledge of working with computers in general is required. My purpose of writing this
was to provide a gentle tutorial on the syntax of SQL, so that the reader is able to recognize the parts
of queries they encounter and even be able to write simple SQL statements and queries themselves.
The book however is not intended as a reference work or for a full time database administrator since
it does not have an exaustive topic coverage.
Your questions, comments, criticism, encouragement and corrections are most welcome and you
can e-mail me at rhlbatra[aht]hotmail[dot]com. I’ll try answering all on-topic mails and will try to
include suggestions, errors and omissions in future editions.
Rahul Batra (8th October 2012)
About the author
Rahul Batra was first introduced to programming in 1996 in GWBASIC, but he did not seriously
foray into it till 2001 when he started learning C++. Along the way, there were dabblings in many
other languages like C, Ruby, Perl and Java. He has worked on Oracle, MySQL, Sybase ASA, Ingres
and SQLite.
Rahul has been programming professionally since 2006 and currently lives and works in Gurgaon,
India.
Acknowledgements
This work would not have been completed without the support of my family and friends. First and
foremost, I owe this book to my son. He has given my life new meaning and direction. A thank
you is in order for my wife Pria, who not only acted as an editor but also constantly supported and
cheered me on to complete it. Many thanks to my parents too, who got me a computer early in life
to start tinkering around with and for constantly encouraging me to pursue my dreams.
Thanks also go out to my sister, niece and nephew (may you have beautiful lives ahead) and
my friends for bringing much happiness into my life. Finally I would like to acknowledge the
contribution of my teachers who helped me form my computing knowledge.
I would also like to acknowledge the contributions of the following readers who suggested
improvements and caught errors I had missed: Keith Thompson, Nathan Adams, Paul Guilbault
and Jim Noh.
1. An Introduction to SQL
A database is nothing but a collection of organized data. It doesn’t have to be in a digital format to
be called a database. A telephone directory is a good example, which stores data about people and
organizations with a contact number. Software which is used to manage a digital database is called
a Database Management System (DBMS).
The most prevalent database organizational model is the Relational Model, developed by Dr. E F
Codd in his groundbreaking research paper - A Relational Model of Data for Large Shared Data
Banksln this model, data to be stored is organized as rows inside a table with the column headings
specifying the corresponding type of data stored. This is not unlike a spreadsheet where the first
row can be thought of as column headings and the subsequent rows storing the actual data.
© What does the word relational in relational
database mean?
It is a common misconception that the word relational implies relationship between the
tables. A relation is a mathematical term that is roughly equivalent to a table itself. When
used in conjunction with the word database, we mean to say that this particular system
arranges data in a tabular fashion.
SQL stands for Structured Query Language and it is the de-facto standard for interacting with
relational databases. Almost all database management systems you’ll come across will have a SQL
implementation. SQL was standardized by the American National Standards Institute (ANSI) in
1986 and has undergone many revisions, most notably in 1992 and 1999. However, all DBMS’s do
not strictly adhere to the standard defined but rather remove some features and add others to provide
a unique feature set. Nonetheless, the standardization process has been helpful in giving a uniform
direction to the vendors in terms of their database interaction language.
1.1 SQL Commands Classification
SQL is a language for interacting with databases. It consists of a number of commands with further
options to allow you to carry out your operations with a database. While DBMS’s differ in the
command subset they provide, usually you would find the classifications below.
• Data Definition Language (DDL) : CREATE TABLE, ALTER TABLE, DROP TABLE etc.
These commands allow you to create or modify your database structure.
An Introduction to SQL
2
• Data Manipulation Language (DML) : INSERT, UPDATE, DELETE
These commands are used to manipulate data stored inside your database.
• Data Query Language (DQL) : SELECT
Used for querying or selecting a subset of data from a database.
• Data Control Language (DCL) : GRANT, REVOKE etc.
Used for controlling access to data within a database, commonly used for granting user
privileges.
• Transaction Control Commands : COMMIT, ROLLBACK etc.
Used for managing groups of statements as a unit of work.
Besides these, your database management system may give you other sets of commands to work
more efficiently or to provide extra features. But it is safe to say that the ones above would be
present in almost all DBMS’s you encounter.
1.2 Explaining Tables
A table in a relational database is nothing but a matrix of data where the columns describe the type
of data and the row contains the actual data to be stored. Have a look at the figure below to get a
sense of the visualization of a table in a database.
Figure: a table describing Programming Languages
id
language
author
year
1
Fortran
Backus
1955
2
Lisp
McCarthy
1958
3
Cobol
Hopper
1959
The above table stores data about programming languages. It consists of 4 columns (id, language,
author and year) and 3 rows. The formal term for a column in a database is a field and a row is
known as a record.
There are two things of note in the figure above. The first one is that, the id field effectively tells you
nothing about the programming language by itself, other than its sequential position in the table.
The second is that though we can understand the fields by looking at their names, we have not
formally assigned a data type to them i.e. we have not restricted (not yet anyways) whether a field
should contain alphabets or numbers or a combination of both.
The id field here serves the purpose of a primary key in the table. It makes each record in the table
unique and its advantages will become clearer in chapters to come. But for now consider this, what if
a language creator made two languages in the same year; we would have a difficult time narrowing
down on the records. An id field usually serves as a good primary key since it’s guaranteed to be
An Introduction to SQL
3
unique, but usage of other fields for this purpose is not restricted.
Just like programming languages, SQL also has data types to define the kind of data that will be
stored in its fields. In the table given above, we can see that the fields language and author must
store English language characters. Thus their data type during table creation should be specified as
varchar which stands for variable number of characters.
The other commonly used data types you will encounter in subsequent chapters are:
Fixed length characters char
Integer values int
Decimal numbers decimal
Date data type date
2. Getting your database ready
2.1 Using Ingres
The best way to learn SQL is to practice writing commands on a real relational database. In this book
SQL is taught using a product called Ingres. The reasons for choosing Ingres are simple - it comes
in a free and open source edition, it’s available on most major platforms and it’s a full-fledged
enterprise class database with many features. However, any relational database product that you
can get your hands on should serve you just fine. There might be minor incompatibilities between
different vendors, so if you choose something else to practice on while reading this book, it would
be a good idea to keep the database vendor’s user manual handy.
Since this text deals largely with teaching SQL in a product independent manner, rather than the
teaching of Ingres per se, details with respect to installation and specific operations of the product
will be kept to a minimum. Emphasis is instead placed on a few specific steps that will help you to
get working on Ingres as fast as possible.
The current version of Ingres during the writing of the book was 10.1 and the Community Edition
has been used on a Windows box for the chapters to follow. The installation itself is straightforward
like any other Windows software. However if you are unsure on any option, ask your DBA (database
administrator, in case one is available) or if you are practicing on a home box - select the ‘Traditional
Ingres’ mode and install the Demo database when it asks you these questions. Feel free to refer to
the Ingres installation guide that is available on the web at the following location. Ingres Installation
Guide 1
If your installation is successful, you should be able to start the Ingres Visual DBA from the Start
Menu. This utility is a graphical user interface to manage your Ingres databases, but we will
keep the usage of this to a minimum since our interest lies in learning SQL rather than database
administration.
2.2 Using SQLite
If installing Ingres seems like a daunting task, you are in luck. There is a very credible, free alternative
database for you to practice on. It is called SQLite and it’s creator D. Richard Hipp has generously
licensed it in the public domain. You can download it from the SQLite Download page 2 .
If you are using Microsoft Windows, you are looking for the section titled Precompiled Binaries
for Windows. Download the SQLite DLL zip archive, named like sqlite-dll-win32-x86-xxxxxxx.zip,
mttp ://docs . actian.com/ingres/ 1 0 . 0/installation- guide
2 http://sqlite.org/download.html
Getting your database ready
5
which contains SQLite but not a way to interact with it. For that you must download the SQLite
shell, named like sqlite-shell-win32-x86-xxxxxxx.zip, which will allow us to create and query SQLite
databases through the command line.
Extract both these archives into the same directory and you are done installing SQLite. Your folder
should now contain atleast three files - sqlite3.dll, sqlite3.def sqlite3.exe. The last one launches the
command shell used to interact with SQLite databases.
2.3 Creating your own database
Most database management systems, including Ingres, allow you to create multiple databases. Lor
practice purposes it’s advisable to create your own database, so that you are free to perform any
operations on it.
Most database systems differ in the way they provide database creation facilities. Ingres achieves the
same by providing you multiple ways to do this, including through the Visual DBA utility. However
for didactic purposes, we will instead use a command operation to create our database. Open up
the Ingres Command Prompt from the program menu (usually found inside Start Menu->Programs-
>Ingres for Microsoft Windows systems), and enter the command as below.
Listing: using createdb and its sample output
1 C : \\Documents and Settings\\rahulb>createdb testdb
2 Creating database 'testdb' . . .
Creating DBMS System Catalogs . . .
4 Modifying DBMS System Catalogs . . .
5 Creating Standard Catalog Interface . . .
6 Creating Front-end System Catalogs . . .
7 Creation of database 'testdb' completed successfully.
The command createdb is used to create a database which will serve as a holding envelope for your
tables. In the example and output shown above, we created a database called testdb for our use. You
(or more specifically your system login) are now the owner of this database and have full control of
entities within it. This is analogous to creating a file in an operating system where the creator gets
full access control rights and may choose to give other users and groups specific rights.
If you are using SQLite, fire up the command shell and you will be greeted with a window with the
text displayed below.
Getting your database ready
6
1 SQLite version 3.8.2 2013-12-06 14:53:30
2 Enter ".help" for instructions
3 Enter SQL statements terminated with a
4 sqlite>
Here we enter our .open command to both create a SQLite database or open it in case it already
exists.
1 .open testdb
If you are using Linux, SQLite does not come with the .open command on it. Instead you directly
write the database name on the terminal immediately after the interactive SQL shell program name
like below.
1 sqlite3 testdb
Turning on column headers in SQLite
SQLite, by default, does not display column headers in the output of a query. This being a
very useful visual helper, I usually turn it on using by two commands below (to be executed
inside the SQLite shell).
sqlite> .mode column sql ite> .headers on
2.4 Table Creation
We have already explored the concept of a table in a relational model. It is now time to create one
using a standard SQL command - CREATE TABLE.
o
The SQL standard by definition allows commands and keywords to be written in a case
insensitive manner. In this book we would use uppercase letters while writing them in
statements, which is a widely accepted practice.
Getting your database ready
7
Listing: General Syntax of a CREATE TABLE statement
1 CREATE TABLE <Table_Name>
2
( <Field
1>
<Data
Type>
3
<Field
2>
<Data
Type>
4
V V
V
5
<Field
N>
<Data
Type>
This is the simplest valid statement that will create a table for you, devoid of any extra options. We’ll
further this with clauses and constraints as we go along, but for now let us use this general syntax
to actually create the table of programming languages we introduced in Chapter 1.
The easiest way to get started with writing SQL statements in Ingres is to use their Visual SQL
application which gives you a graphical interface to write statements and view output. The usual
place to find it on a Windows system is Start -> Programs -> Ingres -> Ingres II -> Other Utilities.
When you open it up, it gives you a set of dropdown boxes on the top half of the window where
you can select the database you wish to work upon and other such options. Since we’ll be using the
same database we created previously (testdb), go ahead and select the options as specified below.
Default User
Default Server INGRES
Database testdb
The actual SQL statement you would be writing to create your table is given below.
Listing: Creating the programming languages table
1 CREATE TABLE proglang_tbl (
2 id INTEGER,
3 language VARCHAR(20),
4 author VARCHAR(25),
5 year INTEGER);
Press the ‘Go’ or F5 button when you’re done entering the statement in full. If you get no errors
back from Visual SQL, then congratulations are in order since you’ve just created your first table.
The statement by itself is simple enough since it resembles the general syntax of CREATE TABLE we
discussed beforehand. It is interesting to note the data types chosen for the fields. Both id and year
are specified as integers for simplicity, even though there are better alternatives. The language field
is given a space of 20 characters to store the name of the programming language while the author
field can hold 25 characters for the creator’s name.
The semicolon at the last position is the delimiter for SQL statements and it marks the end of a
statement.
Getting your database ready
8
The same CREATE TABLE statement also works fine for SQLite and is written in the SQLite
command shell itself.
2.5 Inserting data
The table we have just created is empty so our task now becomes insertion of some sample data
inside it. To populate this data in the form of rows we use the DML command INSERT, whose
general syntax is given below.
Listing: General syntax of INSERT TABLE
1 INSERT INTO <Table Name>
2 VALUES ('Valuel', 'Value2', ...);
Fitting some sample values into this general syntax is simple enough, provided we keep in mind the
structure of the table we are trying to insert the row in. For populating the proglang_tbl with rows
like we saw in chapter 1, we would have to use three INSERT statements as below.
Listing: Inserting data into the proglang tbl table
1 INSERT INTO proglang_tbl VALUES (1, 'Fortran', 'Backus', 1955);
2 INSERT INTO proglang_tbl VALUES (2, 'Lisp', 'McCarthy', 1958);
3 INSERT INTO proglang_tbl VALUES (3, 'Cobol', 'Hopper', 1959);
If you do not receive any errors from Ingres Visual SQL (or the SQL interface for your chosen
DBMS), then you have managed to successfully insert 3 rows of data into your table. Notice how
we’ve carefully kept the ordering of the fields in the same sequence as we used for creating our table.
This strict ordering limitation can be removed and we will see how to achieve that in a little while.
2.6 Writing your first query
Let us now turn our attention to writing a simple query to check the results of our previous operations
in which we created a table and inserted three rows of data into it. For this, we would use a Data
Query Language (DQL) command called SELECT.
A query is simply a SQL statement that allows you to retrieve a useful subset of data contained
within your database. You might have noticed that the INSERT and CREATE TABLE commands
were referred to as statements, but a fetching operation with SELECT falls under the query category.
Most of your day to day operations in a SQL environment would involve queries, since you’d be
creating the database structure once (modifying it only on a need basis) and inserting rows only
when new data is available. While a typical SELECT query is fairly complex with many clauses, we
will begin our journey by writing down a query just to verify the contents of our table. The general
syntax of a simple query is given below.
Getting your database ready
9
Listing: General Syntax of a simple SQL query
1 SELECT <Selection> FROM <Table Name>;
Transforming this into our result verification query is a simple task. We already know the table we
wish to query - proglang_tbl and for our selection we would use * (star), which will select all rows
and fields from the table.
1 SELECT * FROM proglang_tbl ;
The output of this query would be all the (3) rows displayed in a matrix format just as we intended.
If you are running this through Visual SQL on Ingres, you would get a message at the bottom saying
- Total Fetched Row(s): 3.
3. Constraints
A constraint is a rule that you apply or abide by while doing SQL operations. They are useful in
cases where you wish to make the data inside your database more meaningful and/or structured.
Consider the example of the programming languages table - every programming language that has
been created, must have an author (whether a single person, or a couple or a committee). Similarly
it should have a year when it was introduced, be it the year it first appeared as a research paper or
the year a working compiler for it was written. In such cases, it makes sense to create your table in
such a way that certain fields do not accept a NULL (empty) value.
We now modify our previous CREATE TABLE statement so that we can apply the NULL constraint
to some fields.
Listing: Creating a table with NULL constraints
1 CREATE TABLE prog lang_tbl copy (
2
id
INTEGER
NOT
NULL,
3
language
VARCHAR(20)
NOT
NULL,
4
author
VARCHAR(25)
NOT
NULL,
5
year
INTEGER
NOT
NULL,
6
standard
VARCHAR(10)
NULL);
We see in this case that we have achieved our objective of creating a table in which the field’s id,
language, author and year cannot be empty for any row, but the new field standard can take empty
values. We now go about trying to insert new rows into this table using an alternative INSERT
syntax.
3.1 Selective fields INSERT
From our last encounter with the INSERT statement, we saw that we had to specify the data to be
inserted in the same order as specified during the creation of the table in question. We now look at
another variation which will allow us to overcome this limitation and handle inserting rows with
embedded NULL values in their fields.
1
2
3
4
5
6
7
8
9
10
11
12
13
1
2
3
4
5
6
7
8
Constraints
11
Listing: General Syntax of INSERT with selected fields
INSERT
INTO
<Table_Name>
( <Field
Name
1>,
<Field
Name
2>,
<Field
Name
N> )
VALUES
( <Value
For
Field
1>
<Value
For
Field
2>
<Value
For
Field
N>
Since we specify the field order in the statement itself, we are free to reorder the values sequence
in the same statement thus removing the first limitation. Also, if we wish to enter a empty (NULL)
value in any of the fields for a record, it is easy to do so by simply not including the field’s name in
the first part of the statement. The statement would run fine without specifying any fields you wish
to omit provided they do not have a NOT NULL constraint attached to them. We now write some
INSERT statements for the proglang_tblcopy table, in which we try to insert some languages which
have not been standardized by any organizations and some which have been.
Listing: Inserting new data into the proglang tblcopy table
INSERT INTO proglang_tblcopy (id, language, author, year, standard)
VALUES (1, 'Prolog', ' Colmerauer ’ , '1972', ’ISO');
INSERT INTO proglang_tblcopy (id, language, author, year)
VALUES (2, 'Perl', 'Wall', '1987');
INSERT INTO proglang_tblcopy (id, year, standard, language, author)
VALUES (3, '1964', 'ANSI', 'APL', 'Iverson');
When you run this through your SQL interface, 3 new rows would be inserted into the table. Notice
the ordering of the third row; it is not the same sequence we used to create the table. Also Perl has
not been standardized by an international body, so we do not specify the field name itself while
doing the INSERT operation.
To verify the results of these statements and to make sure that the correct data went into the correct
fields, we run a simple query as before.
Constraints
12
1 SELECT * FROM prog lang_tbl copy;
Figure: Result of the query run on proglangtblcopy
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
3.2 Primary Key Constraint
A primary key is used to make each record unique in atleast one way by forcing a field to have
unique values. They do not have to be restricted to only one field, a combination of them can also
be defined as a primary key for a table. In our programming languages table, the id field is a good
choice for applying the primary key constraint. We will now modify our CREATE TABLE statement
to incorporate this.
Listing: a CREATE TABLE statement with a primary key
1
2
3
4
5
6
CREATE TABLE prog lang_tbl imp (
id
INTEGER
NOT NULL PRIMARY KEY,
language
VARCHAR(20)
NOT NULL,
author
VARCHAR(25)
NOT NULL,
year
INTEGER
NOT NULL,
standard
VARCHAR(10)
NULL);
ID fields are usually chosen as primary fields. Note that in this particular table, the language field
would have also worked, since a language name is unique. However, if we have a table which
describes say people - since two people can have the same name, we usually try to find a unique
field like their SSN number or employee ID number.
3.3 Unique Key Constraint
A unique key like a primary key is also used to make each record inside a table unique. Once you
have defined the primary key of a table, any other fields you wish to make unique is done through
this constraint. For example, in our database it now makes sense to have a unique key constraint on
the language field. This would ensure none of the records would duplicate information about the
same programming language.
Constraints
13
Listing: a CREATE TABLE statement with a primary key and a unique constraint
1 CREATE TABLE proglang_tbluk (
2 id INTEGER NOT NULL PRIMARY KEY,
3 language VARCHAR(20) NOT NULL UNIQUE,
4 author VARCHAR(25) NOT NULL,
5 year INTEGER NOT NULL,
6 standard VARCHAR(10) NULL);
Note that we write the word UNIQUE in front of the field and omit the KEY in this case. You can
have as many fields with unique constraints as you wish.
3.4 Differences between a Primary Key and a Unique
Key
You might have noticed that the two constraints discussed above are similar in their purpose.
However, there are a couple of differences between them.
1. A primary key field cannot take on a NULL value, whereas a field with a unique constraint
can. However, there can be only one such record since each value must be unique due to the
very definition of the constraint.
2. You are allowed to define only one primary key constraint but you can apply the unique
constraint to as many fields as you like.
4. Operations on Tables
You might have noticed that we keep on making new tables whenever we are introducing a new
concept. This has had the not-so desirable effect of populating our database with many similar tables.
We will now go about deleting unneeded tables and modifying existing ones to suit our needs.
4.1 Dropping Tables
The deletion of tables in SQL is achieved through the DROP TABLE command. We will now drop
any superfluous tables we have created during the previous lessons.
Listing: dropping the temporary tables we created
1 DROP TABLE proglang_tbl ;
2
3 DROP TABLE prog lang_tbl copy ;
4
5 DROP TABLE prog lang_tbl imp;
4.2 Creating new tables from existing tables
You might have noticed that we have dropped the proglang_tbl table and we now have with us
only the proglang_tbluk table which has all the necessary constraints and fields. The latter’s name
was chosen when we were discussing the unique key constraint, but it now seems logical to migrate
this table structure (and any corresponding data) back to the name proglang_tbl. We achieve this by
creating a copy of the table using a combination of both CREATE TABLE and SELECT commands
and learn a new clause AS.
Listing: general syntax for creating a new table from an existing one
1 CREATE TABLE <New Table> AS SELECT <Selection> FROM <01d Table> ;
Since our proglang_tbluk contains no records, we will push some sample data in it so that we can
later verify whether the records themselves got copied or not. Notice that we would have to give
the field names explicitly, else the second row (which contains no standard field value) would give
an error similar to ‘number of target columns must equal the number of specified values’ in Ingres.
Operations on Tables
15
Listing: inserting some data into the proglangtbluk table
1 INSERT INTO proglang_tbluk (id, language, author, year, standard)
2 VALUES (1, 'Prolog' , ' Colmerauer 1 , '1972', ’ISO');
3
4 INSERT INTO proglang_tbluk (id, language, author, year)
5 VALUES (2, 'Perl', 'Wall', '1987');
6
7 INSERT INTO proglang_tbluk (id, year, standard, language, author)
8 VALUES (3, '1964', 'ANSI', 'APL', 'Iverson');
To create an exact copy of the existing table, we use the same selection criteria as we have seen before
- * (star). This will select all the fields from the existing table and create the new table with them
alongwith any records. It is possible to use only a subset of fields from the old table by modifying
the selection criteria and we will see this later.
Listing: recreating a new table from an existing one
1 CREATE TABLE proglang_tbl AS SELECT * FROM proglang_tbluk;
We now run a simple SELECT query to see whether our objective was achieved or not.
1 SELECT * FROM proglang_tbl ;
Figure: Result of the query run on proglang tbl
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
4.3 Modifying tables
After a table has been created, you can still modify its structure using the ALTER TABLE command.
What we mean by modify is that you can change field types, sizes, even add or delete columns. There
are some rules you have to abide by while altering a table, but for now we will see a simple example
to modify the field author for the proglang_tbl table.
Operations on Tables
16
Listing: General syntax of a simple ALTER TABLE command
1 ALTER TABLE <Table name> <Operation> <Field with clauses>;
We already know that we are going to operate on the proglang_tbl table and the field we wish to
modify is author which should now hold 30 characters instead of 25. The operation to choose in this
case is ALTER which would modify our existing field.
Listing: Altering the author field
1 ALTER TABLE proglang_tbl ALTER author varchar(30);
4.4 Verifying the result in Ingres
While one option to verify the result of our ALTER TABLE command is to run an INSERT statement
with the author’s name greater than 25 characters and verify that we get no errors back, it is a
tedious process. In Ingres specifically, we can look at the Ingres Visual DBA application to check
the columns tab in the testdb database. However, another way to verify the same using a console
tool is the isql command line tool available through the Ingres Command Prompt we used earlier
for database creation.
To launch isql (which stands for Interactive SQL) using the Ingres command prompt we type:
1 isql testdb
The first argument we write is the database we wish to connect to. The result of running this
command is an interactive console window where you would be able to write SQL statements and
verify the results much like Visual SQL. The difference between the two (other than the obvious
differences in the user interface) is that isql allows you access to the HELP command, which is what
we will be using to verify the result of our ALTER TABLE statement. In the interaction window that
opens up, we write the HELP command as below and the subsequent box shows the output of the
command.
HELP TABLE proglang_tbl ;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Operations on Tables
17
Figure: the result of running the HELP TABLE command
Name :
Owner :
Created
Location
Type:
Version :
Page size:
Cache priority:
Alter table version
Alter table totwidth
Row width
Number of rows:
Storage structure:
Compression :
Duplicate Rows:
Number of pages:
Overflow data pages:
Journaling
Base table for view
Permissions
Integrities :
Optimizer statistics:
proglang_tbl
rahulb
20- feb 2012 17:04:28
i i_database
user table
1110. 0
8192
0
4
76
76
3
heap
none
allowed
3
0
enabled after the next checkpoint
no
none
none
none
Column Information:
I Column Name
1 Type
1
1 id
1 integer
1 language
I varchar
1 author
I varchar
1 year
1 integer
I standard
varchar
Secondary indexes:
1 Length
1
iNulls
|
I Defaults
1
1 4
I no
I no
|20
no
I no
|30
lyes
1 null
1 4
I no
I no
|10
lyes
1 null
none
Key Seq
While there is a lot of information in the result, we are currently interested in the Column
Information section which now displays the new length of the author field, i.e. 30. But it is also
important to note that our ALTER TABLE statement just removed the not-null constraint from the
field. To retain the same, we would have to specify the constraint in the alter command since the
default behavior is to allow NULL values.
Operations on Tables
18
4.5 Verifying the result in other DBMS's
The HELP command we just saw is specific to the Ingres RDBMS, it is not a part of the SQL
standard. To achieve the same objective on a different RDBMS like Oracle, you are provided with
the DESCRIBE command which allows you to view a table definition. While the information this
command show may vary from one DBMS to another, they at least show the field name, its data
type and whether or not NULL values are allowed for the particular field. The general synatax of
the command is given below.
Listing: the general syntax of the DESCRIBE statement
1 DESCRIBE < table name> ;
4.6 Showing table information in SQLite
SQLite as of the writing of this text does not support modification to column sizes in a table using
ALTER TABLE. It does however allow you to view table and column information.
SQLite has it’s own special dot syntax commands which allow certain useful database management
tasks. We have already seen the . open command used to create and open a database. Similarly we
can use the . schema command to get table information.
Listing: showing table and column information in SQLite
1 sqlite> .schema proglang_tbl
2
3 CREATE TABLE proglang_tbl (
4 id INTEGER NOT NULL PRIMARY KEY,
5 language VARCHAR(20) NOT NULL UNIQUE,
6 author VARCHAR(25) NOT NULL,
7 year INTEGER NOT NULL,
8 standard VARCHAR(10) NULL);
5. Writing Basic Queries
A query is a SQL statement that is used to extract a subset of data from your database and presents
it in a readable format. As we have seen previously, the SELECT command is used to run queries in
SQL. You can further add clauses to your query to get a filtered, more meaningful result. Since the
majority of operations on a database involve queries, it is important to understand them in detail.
While this chapter will only deal with queries run on a single table, you can run a SELECT operation
on multiple tables in a single statement.
5.1 Selecting a limited number of columns
We have already seen how to extract all the data from a table when we were verifying our results in
the previous chapters. But as you might have noted - a query can be used to extract a subset of data
too. We first test this by limiting the number of fields to show in the query output by not specifying
the * selection criteria, but by naming the fields explicitly.
Listing: selecting a subset of fields from a table
SELECT language, year FROM proglang_tbl ;
Figure: Output of running the chosen fields SELECT query
language
year
Prolog
1972
Perl
1987
APL
1964
You can see that the query we constructed mentioned the fields we wish to see, i.e. language and
year. Also note that the result of this query is useful by itself as a report for looking at the chronology
of programming language creation. While this is not a rule enforced by SQL or a relation database
management system, it makes sense to construct your query in such a way that the meaning is self-
evident if the output is meant to be read by a human. This is the reason we left out the field id in
the query, since it has no inherent meaning to the reader except if they wish to know the sequential
order of the storage of records in the table.
Writing Basic Queries
20
5.2 Ordering the results
You might have noticed that in our previous query output, the languages were printed out in the
same order as we had inserted them. But what if we wanted to sort the results by the year the
language was created in. The chronological order might make more sense if we wish to view the
development of programming languages through the decades. In such cases, we take the help of the
ORDER BY clause. To achieve our purpose, we modify our query with this additional clause.
Listing: Usage of the ORDER BY clause
1 SELECT language, year FROM proglang_tbl ORDER BY year;
Figure: Output of the ordered SELECT query
language
year
APL
1964
Prolog
1972
Perl
1987
The astute reader will notice that the output of our ORDER BY clause was ascending. To reverse
this, we add the argument DESC to our ORDER BY clause as below.
Listing: Usage of the ORDER BY clause with the DESC argument
1 SELECT language, year FROM proglang_tbl ORDER BY year DESC;
Figure: Output of the ordered SELECT query in descending order
language
year
Perl
1987
Prolog
1972
APL
1964
5.3 Ordering using field abbreviations
A useful shortcut in SQL involves ordering a query result using an integer abbreviation instead of
the complete field name. The abbreviations are formed starting with 1 which is given to the first
field specified in the query, 2 to the second field and so on. Rewriting our above query to sort the
output by descending year, we get:
Writing Basic Queries
21
1 SELECT language, year FROM proglang_tbl ORDER BY 2 DESC;
Figure: Output of the ordered SELECT query in
language
descending order using field abbreviations
year
Perl
1987
Prolog
1972
APL
1964
The 2 argument given to the ORDER BY clause signifies ordering by the second field specified in
the query, namely year.
5.4 Putting conditions with WHERE
We have already seen how to select a subset of data available in a table by limiting the fields queried.
We will now limit the number of records retrieved in a query using conditions. The WHERE clause
is used to achieve this and it can be combined with explicit field selection or ordering clauses to
provide meaningful output.
For a query to run successfully, it must have atleast two parts - the SELECT and the FROM clause.
After this we place the optional MIHERE condition and then the ordering clause. Thus, if we wanted
to see the programming language (and it’s author) which was standardized by ANSI, we’d write our
query as below.
Listing: Using a WHERE conditional
1 SELECT language, author FROM proglang_tbl WHERE standard = 'ANSI';
As you may have noticed, the query we forulated specified the language and author fields, but the
condition was imposed on a separate field altogether - standard. Thus we can safely say that while
we can choose what columns to display, our conditionals can work on a record with any of its fields.
Figure: Output of the SELECT query with a WHERE conditional clause
language author
APL Iverson
You are by no means restricted to use = (equals) for your conditions. It is perfectly acceptable to
choose other operators like < and >. You can also include the ORDER BY clause and sort your output.
An example is given below.
Writing Basic Queries
22
Listing: Combining the WHERE and ORDER BY
1 SELECT language, author, year FROM proglang_tbl WHERE year > 1970 ORDER BY autho\
2 r;
Figure: Output of the SELECT query with a WHERE and ORDER BY
language author year
Prolog Colmerauer 1972
Perl Wall 1987
Notice that the output only shows programming languages developed after 1970 (atleast according
to our database). Also since the ordering is done by a varchar field, the sorting is done alphabetically
in an ascending order.
5.5 Combining conditions
If we can only specify one condition using the 'WHERE clause, it will fulfill only a tiny fraction of
real world requirements. We can however construct complex conditions using the boolean operators
AND and OR.
When we want our resultset to satisfy all of the multiple conditions, we use the AND operator.
Listing: using the AND operator to combine conditions
1 SELECT language, author, year FROM proglang_tbl WHERE year > 1970 AND standard I\
2 S NULL;
language author year
Perl Wall 1987
Tel Ousterhout 1988
The result satisfies both the conditions we specified, namely - the language should not be standard-
ized and it must have been created after 1970.
If we want our resultset to satisfy any one of our conditions, we use the OR operator.
Writing Basic Queries
23
Listing: using the OR operator
1 SELECT language, author, year FROM proglang_tbl WHERE year > 1970 OR standard IS\
2 NULL;
language
author
year
Prolog
Colmerauer
1972
Perl
Wall
1987
Tel
Ousterhout
1988
The result now contains languages which were created either after 1970 like Prolog or which are not
standardized like Perl or Tel. In this particular example, the first condition satisfies all the rows of the
resultset. But if there were a language which was created before 1970 and wasn’t yet standardized, it
would show up as a result of this query. We can even create yet more complex queries by combining
these operators.
6. Manipulating Data
In this chapter we study the Data Manipulation Language (DML) part of SQL which is used to
make changes to the data inside a relational database. The three basic commands of DML are as
follows.
INSERT Populates tables with new data
UPDATE Updates existing data
DELETE Deletes data from tables
We have already seen a few examples on the INSERT statement including simple inserts and selective
field insertions. Thus we will concentrate on other ways to use this statement.
6.1 Inserting NULL's
In previous chapters, we have seen that not specifying a column value while doing selective field
insert operations results in a null value being set for them. We can also explicitly use the keyword
NULL in SQL to signify null values in statements like INSERT.
Listing: Inserting NULL values
1 INSERT INTO proglang_tbl VALUES (4, ’Tcl’, ’ Ousterhout 1 , ’1988', NULL);
Running a query to show the contents of the entire table helps us to verify the result.
1 SELECT * FROM proglang_tbl ;
Figure: a table with NULL values
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
4
Tcl
Ousterhout
1988
(null)
Manipulating Data
25
6.2 Inserting data into a table from another table
You can insert new records into a table from another one by using a combination of INSERT and
SELECT. Since a query would return you some records, combining it with an insertion command
would enter these records into the new table. You can even use a MIHERE conditional to limit or filter
the records you wish to enter into the new table. We will now create a new table called stdlang_tbl,
which will have only two fields - language and standard. In this we would insert rows from the
proglang_tbl table which have a non-null value in the standard field. This will also demonstrate
our first use of a boolean operator - NOT.
Listing: Using INSERT and SELECT to conditionally load data into another table
1 CREATE TABLE stdlang_tbl (language varchar(20), standard varchar (10));
2
3 INSERT INTO stdlang_tbl SELECT language, standard FROM proglang_tbl WHERE standa\
4 rd IS NOT NULL;
When you view the contents of this table, you will notice that it has picked up the two languages
which actually had a standard column value.
Figure: Contents of the stdlang tbl table
language standard
Prolog ISO
APL ANSI
6.3 Updating existing data
To modify some data in a record, we use the UPDATE command. While it cannot add or delete
records (those responsibilities are delegated to other commands), if a record exists it can modify
its data even affecting multiple fields in one go and applying conditions. The general syntax of an
UPDATE statement is given below.
Manipulating Data
26
Listing: General Syntax of the UPDATE command
1 UPDATE <table_name> SET
2 <columnl> = <value>,
3 <column2> = <value>,
4 <column3> = <value>
5 ...
6 WHERE <condition>;
Let us now return to our proglang_tbl table and add a new row about the Forth programming
language.
1 INSERT INTO proglang_tbl VALUES (5, 'Forth', 'Moore', 1973, NULL);
We later realize that the language actually was created near 1972 (instead of 1973) and it actually
has been standardized in 1994 by the ANSI. Thus we write our update query to reflect the same.
Listing: Updating multiple fields in a single statement
1 UPDATE proglang_tbl SET year = 1972,
2 standard = 'ANSI' WHERE language = 'Forth';
If you’ve typed the statement correctly and no errors are thrown back, the contents of the record in
question would have been modified as intended. Verifying the result of the same involves a simple
query the likes of which we have seen in previous examples.
6.4 Deleting data from tables
You can use the DELETE command to delete records from a table. This means that you can choose
which records you want to delete based on a condition, or delete all records but you cannot delete
certain fields of a record using this statement. The general syntax of the DELETE statement is given
below.
Listing: General syntax of DELETE
1 DELETE FROM <table_name> WHERE <condition>;
While putting a conditional clause in the DELETE is optional, it is almost always used. Simply
because not using it would cause all the records to be deleted from a table, which is a rarely valid
need. We now write the full statement to delete the record corresponding to Forth from the table.
Manipulating Data
27
Listing: Deleting a record from the proglang tbl table
DELETE FROM proglang_tbl WHERE language = 'Forth';
Figure: table contents after the record deletion
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
4
Tel
Ousterhout
1988
(null)
7. Organizing your data
The number of fields you wish to store in your database would be a larger value than the five column
table we saw earlier chapters. Also, some assumptions were made intrinsically on the kind of data
we will store in the table. But this is not always the case in real life. In reality the data we encounter
will be complex, even redundant. This is where the study of data modelling techniques and database
design come in. While it is advised that the reader refer to a more comprehensive treatise on this
subject, nonetheless we will try to study some good relational database design principles since the
study would come in handy while learning SQL statements for multiple tables.
7.1 Normalization
Let us suppose we have a database of employees in a fictional institution as given below. If the
database structure has not been modelled but has been extracted from a raw collection of information
available, redundancy is expected.
Figure: the fictional firm’s database
employeeid
name
skill
managerid
location
1
Socrates
Philosophy
(null)
Greece
2
Plato
Writing
1
Greece
3
Aristotle
Science
2
Greece
4
Descartes
Philosophy
(null)
France
4
Descartes
Philosophy
(null)
Netherlands
We can see that Descartes has two rows because he spent his life in both France and Netherlands.
At a later point we decide that we wish to classify him with a different skill, we would have to
update both rows since they should contain an identical (primary) skill. It would be easier to have
a separate table for skills and and somehow allow the records which share the same skill to refer to
this table. This way if we wish to reflect that both Socrates and Descartes were thinkers in Western
Philosophy renaming the skill record in the second table would do the trick.
This process of breaking down a raw database into logical tables and removing redundancies is
called Normalization. There are even levels of normalization called normal forms which dictate
on how to acheive the desired design.
Organizing your data
29
7.2 Atomicity
In the programming language examples we’ve seen, our assumption has always been that a language
has a single author. But there are countless languages where multiple people contributed to the core
design and should rightfully be acknowledged in our table. How would we go about making such a
record? Let us take the case of BASIC which was designed by John Kemeny and Thomas Kurtz. The
easiest option to add this new record into the table is to fit both author’s in the author field.
Figure: a record with a non-atomic field value
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
4
Tel
Ousterhout
1988
(null)
5
BASIC
Kemeny, Kurtz
1964
ANSI
You can immediately see that it would be difficult to write a query to retrieve this record based on
the author field. If the data written as “Kemeny, Kurtz” or “Kurtz, Kemeny” or even “Kemeny &
Kurtz”, it would be extremely difficult to put the right string in the 'WHERE conditional clause of the
query. This is often the case with multiple values, and the solution is to redesign the table structure
to make all field value atomic .
7.3 Repeating Groups
Another simple (but ultimately wrong) approach that comes to mind is to split the author field into
two parts - author 1 and author2. If a language has only one author, the author2 field would contain
a null value. Can you spot the problem that will arise from this design decision?
Figure: a table with a repeating group
id
language
authorl
author2
year
standard
1
Prolog
Colmerauer
(null)
1972
ISO
2
Perl
Wall
(null)
1987
(null)
3
APL
Iverson
(null)
1964
ANSI
4
Tel
Ousterhout
(null)
1988
(null)
5
BASIC
Kemeny
Kurtz
1964
ANSI
This imposes an artificial constraint on how many authors a language can have. It seems to work
fine for a couple of them, but what if a programming language was designed by a committee of a
dozen or more people? At the database design time, how do we fix the number of authors we wish
Organizing your data
30
to support? This kind of design is referred to as a repeating group and must be actively avoided.
7.4 Splitting the table
The correct design to remove the problems listed above is to split the table into two - one holding
the author details and one detailing the language.
Figure: a table holding author details
authorid
author
languageid
1
Colmerauer
1
2
Wall
2
3
Ousterhout
4
4
Iverson
3
5
Kemeny
5
6
Kurtz
5
Figure: a table holding programming language details
id
language
year
standard
1
Prolog
1972
ISO
2
Perl
1987
(null)
3
APL
1964
ANSI
4
Tel
1988
(null)
5
BASIC
1964
ANSI
Once you have removed the non-atomicity of fields and repeating groups alongwith assigning
unique id’s to your tables, your table structure is now in the first normal form. The author table’s
language_id field which refers to the id field of the language table is called a foreign key constraint.
Listing: creating the new programming languages table
CREATE TABLE
newlang_tbl
(id INTEGER
NOT
NULL PRIMARY KEY,
language VARCHAR(20)
NOT
NULL,
year INTEGER
NOT
NULL,
standard VARCHAR(10)
NULL);
1
2
3
1
2
3
4
1
2
3
4
5
1
2
3
4
Organizing your data
31
Listing: creating the authors table
CREATE TABLE authors_tbl (authored INTEGER NOT NULL,
author VARCHAR(25) NOT NULL,
language_id INTEGER REFERENCES newlang_tbl ( id) ) ;
Notice that in the author’s table we’ve made a foreign key constraint by making the language_id field
reference the id field of the new programming languages table using the keyword REFERENCES.
You can only create a foreign key reference a primary or unique key, otherwise during the constraint
creation time we would recieve an error similar to the following.
E_PS0490 CREATE/ALTER TABLE: The referenced columns in table ' newlang_tbl '
do not form a unique constraint; a foreign key may only reference
columns in a unique or primary key constraint.
(Thu May 17 15:28:45 2012)
Since we have created a reference to the language_id, inserting a row in the author’s table which does
not yet have a language entry would also result in an error, called a Referential Integrity violation.
INSERT INTO authors_tbl (author_id, author, language_id) VALUES (5, 'Kemeny', 5)
E_US1906 Cannot INSERT into table ' "authors_tbl " ' because the values do
not match those in table ' "newlang_tbl " ' (violation of REFERENTIAL
constraint ' "$autho_r0000010c00000000" 1 ) .
However when done sequentially, i.e. the language first and then its corresponding entry in the
author table, everything works out.
Listing: making entries for BASIC in both the tables
INSERT INTO newlang_tbl (id, language, year, standard) VALUES (5, ’BASIC', 1964, \
’ANSI ' );
INSERT INTO authors_tbl (author_id, author, language_id) VALUES (5, ’Kemeny’, 5);
The other statements to get fully populated tables are given below.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Organizing your data
32
INSERT INTO newlang
72, 'ISO');
INSERT INTO newlang
INSERT INTO newlang
'ANSI 1 );
INSERT INTO newlang
tbl
(id,
language,
tbl
(id,
language,
tbl
(id,
language,
tbl
(id,
language,
year, standard)
year) VALUES (2,
year, standard)
year) VALUES (4,
VALUES
’Perl '
VALUES
' Tel
INSERT
INTO
authors.
_tbl
(author_id,
author,
language_id)
VALUES
INSERT
, i);
INSERT
INTO
authors.
_tbl
(author_id,
author,
language_id)
VALUES
INTO
authors.
_tbl
(author_id,
author,
language_id)
VALUES
INSERT
, 4);
INSERT
INTO
authors.
_tbl
(author_id,
author,
language_id)
VALUES
INTO
authors.
_tbl
(author_id,
author,
language_id)
VALUES
);
(1, 'Prolog', 19\
1987);
(3, 'APL', 1964, \
1988);
(6, ’ Kurtz 1 , 5) ;
(1, ' Colmerauer ' \
(2, 'Wall', 2);
(3, 1 Ousterhout ' \
(4, ' Iverson ' , 3\
8. Doing more with queries
We have already seen some basic queries, how to order the results of a query and how to put
conditions on the query output. Let us now see more examples of how we can modify our SELECT
statements to suit our needs.
8.1 Counting the records in a table
Sometimes we just wish to know how many records exist in a table without actually outputting
the entire contents of these records. This can be achieved through the use of a SQL function called
COUNT. Let us first see the contents of the proglang_tbl table.
Figure: contents of our programming languages table
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
3
APL
Iverson
1964
ANSI
4
Tel
Ousterhout
1988
(null)
Listing: Query to count number of records in the table
1 SELECT COUNT(*) FROM proglang_tbl ;
The output returned will be a single record with a single field with the value as 4. The function
COUNT took one argument i.e. what to count and we provided it with * which means the entire
record. Thus we achieved our purpose of counting records in a table.
What would happen if instead of giving an entire record to count, we explicitly specify a column?
And what if the column had null values? Let’s see this scenario by counting on the standard field
of the table.
Listing: Query to count number of standard field values in the table
1 SELECT COUNT (standard) FROM proglang_tbl ;
The output in this case would be the value 2, because we only have two records with non-null values
in the standard field.
Doing more with queries
34
8.2 Column Aliases
Queries are frequently consumed directly as reports since SQL provides enough functionality to give
meaning to data stored inside a RDBMS. One of the features allowing this is Column Aliases, which
let you rename column headings in the resultant output. The general syntax for creating a column
alias is given below.
Listing: General Syntax for creating column aliases
1 SELECT <columnl> <aliasl>, <column2> <alias2> ... from <table> ;
For example, we wish to output our programming languages table with a few columns only. But we
do not wish to call the authors of the language as authors. The person wanting the report wishes
they be called creators. This can be simply done by using the query below.
Listing: Renaming the author field to creator for reporting purposes
1 SELECT id, language, author creator from proglang_tbl ;
While creating a column alias will not permanantly rename a field, it will show up in the resultant
output.
id
1
2
3
4
language
Prolog
Perl
APL
Tel
Figure: the column alias output
creator
Colmerauer
Wall
Iverson
Ousterhout
8.3 Order of execution of SELECT queries
A query is not evaluated from left to right, there is a specific sequence in which its various parts are
evaluated as given below.
1. FROM clause
2. WHERE clause
3. GROUP BY clause
4. HAVING clause
5. SELECT clause
Doing more with queries
35
6. ORDER BY clause
There is an interesting corollary of having the SELECT evaluation being lower (read later) than the
WHERE clause. Most database management systems, like Microsoft SQL Server will not allow you
to use a column alias in the filtering conditions. So a query like the one given below would not work.
Listing: using a column alias in the WHERE clause
1 SELECT author Scientist FROM authors_tbl WHERE Scientist = 'Wall';
However, this works fine in SQLite. Yet another example of how there exists subtle differences
between DBMSs’.
8.4 Using the LIKE operator
While putting conditions on a query using WHERE clauses, we have already seen comparison
operators = and IS NULL. Now we take a look at the LIKE operator which will help us with wildcard
comparisons. For matching we are provided with two wilcard characters to use with LIKE.
1) % (Percent) Used to match multiple characters including a single character
and no character
2) _ (Underscore) Used to match exactly one character
We will first use the % character for wildcard matching. Let us suppose we wish to list out languages
that start with the letter P.
Listing: using the LIKE operator and % wildcard
1 SELECT * FROM proglang_tbl WHERE language LIKE ' P% ’ ;
The output of the above query should be all language records whose name begins with the letter
capital P. Note that this would not include any language that starts with the small letter p.
Figure: all languages starting with P
id
language
author
year
standard
1
Prolog
Colmerauer
1972
ISO
2
Perl
Wall
1987
(null)
We can see that using the % wildcard allowed us to match multiple characters like erl in the case of
Perl. But what if we wanted to restrict how many characters we wished to match? What if our goal
was to write a query which displays the languages ending in the letter l, but are only 3 characters
Doing more with queries
36
in length? The first condition could have been satisfied using the pattern %l, but to satisfy both
conditions in the same query we use the _ wildcard. A pattern like %l would result in returning both
Perl and Tel but we modify our pattern suitably to return only the latter.
SELECT * FROM proglang_tbl WHERE language LIKE ' 1';
Figure: output for _ wildcard matching
id language author year standard
4 Tel Ousterhout 1988 (null)
Note that the result did not include Perl since we explicitly gave two underscores to match 2
characters only. Also it did not match APL since SQL data is case sensitive and 1 is not equal to
L.
9. Calculated Fields
We have already seen column aliases which allow us to rename a field’s name in the query output.
But we frequently encounter conditions which require changes to a field value. This is where the
concept of a calculated field comes in.
9.1 Mathematical calculations
Any numeric field can be operated upon by mathematical operators we are all familiar with. We
can add, subtract, multiply, divide and even find the remainder of a division operation fairly easily.
While the operators supported differ in various implementations, the ones given below should be
available across any RDBMS you come across.
Addition +
Subtraction
Multiplication
Division /
Remainder %
Let us take our programming languages table and try to find out the decade in which the language
was created. For example, Prolog was created in the 1970’s decade. Let us try to find out this fact
from the year of creation available to us. One approach is to find the remainder of the year when
divided by 10, which is the number of years in a decade. This is the value that specifies how many
years has it been since the start of that decade.
select language, (year%10) remain from proglang_tbl ;
language
Prolog
Perl
APL
Tel
remain
2
7
4
8
Now if we subtract this value from the year of creation itself, we would get the decade in which the
programming language was created.
Calculated Fields
38
1 select language, year- (year%10) decade from proglang_tbl ;
language
decade
Prolog
1970
Perl
1980
APL
1960
Tel
1980
Another approach is to divide the year by 10 and then multiply it by 10. This is slightly less
straightforward because it relies on the definition of the integer data type. Since an integer cannot
store decimal points, division by ten would silently chop off the remainder. 1972 divided by 10 would
be 197 discarding the .2 bit. If we multiply this value by 10, we would get our desired decade value.
1 select language, (year/10)*10 decade from proglang_tbl ;
language
decade
Prolog
1970
Perl
1980
APL
1960
Tel
1980
9.2 String operations
By far the most commonly used string operation is concatenation. It means to join or combine
strings. However since even numeric fields can be treated as a string, we can use the concatenation
operator || on them too. See the example below to modify our decade field to include some characters.
1 select language, 'The ' I I ( ( year/1 0 ) *10 ) | | 's' decade from proglang_tbl ;
language decade
Prolog The 1970s
Perl The 1980s
APL The 1960s
Tel The 1980s
Note that the concatenation operator manifests itself in different forms in different implementations.
SQLite and Oracle use the shown || symbols whereas Ingres, MySQL and Microsoft SQL Server use
+ to denote concatenation. Their effect however is the same.
Calculated Fields
39
9.3 Literal Values
There are cases when one needs to use a fixed literal value as the values of a new column. Like
column aliases can change the column header for readability, literal values change record values.
In a sense they are not calculated fields, but fixed fields inserted in specific positions of a record.
An example will help illustrate this - supposing to wish to really clarify that the year of language
creation, as not just a number but also to include the characters AD
select language, year, 'AD' from proglang_tbl ;
language
year
‘AD
Prolog
1972
AD
Perl
1987
AD
APL
1964
AD
Tel
1988
AD
We can even use numeric literal values the same way, omitting the quotation marks for such values.
A common utility for literal values arises when the user has to copy-paste data from their database
query output into another tool like a spreadsheet or wordprocessor.
10. Aggregation and Grouping
10.1 Aggregate Functions
An aggregate function is used to compute summarization information from a table or tables. We have
already seen the COUNT aggregate function which counts the records matched. Similarly there are
other aggregation functions in SQL like AVG for calculating averages, SUM for computing totals
and MAX, MIN for finding out maxima and minima values respectively.
10.2 Using DISTINCT with COUNT
We have already seen the COUNT function, but we can further control its output using the optional
argument DISTINCT This allows us to count only non-duplicate values of the input specified. To
illustrate this concept, we will now insert some rows into our proglang_tbl table.
Listing: Inserting some new rows in our programming languages table
1 INSERT INTO proglang_tbl (id, language, author, year, standard) VALUES (5, ' Fort\
2 ran', 'Backus', 1957, 'ANSI');
3
4 INSERT INTO proglang_tbl (id, language, author, year, standard) VALUES (6, ' PL/ I \
5 ', 'IBM', 1964, ' ECMA' ) ;
Note the new data choice that we are populating. With Fortran we are adding a new programming
language that has a standard by the ANSI. With PL/I we now have a third distinctive standards
organisation - ECMA. PL/I also shares the same birth year as APL (1964) giving us a duplicate year
field. Now let us run a query to check how many distinct year and standard values we have.
Listing: Counting distinct year values
1 SELECT COUNT (DISTINCT year) FROM proglang_tbl ;
2
3 > 5
Aggregation and Grouping
41
Listing: Counting distinct standard values
1 SELECT COUNT (DISTINCT standard) FROM proglang_tbl ;
2
3 > 3
The first query result is straightforward. We have 6 rows but two of them share a common year
value, thus giving us the result 5. In the second result, out of 6 rows only 4 of them have values. Two
rows have a NULL value in them meaning those languages have no standard. Among the 4 rows,
two of them share a common value, giving us the result - 3. Note that the DISTINCT clause did not
count NULL values as truly distinct values.
10.3 Using MIN to find minimum values
The MIN function is fairly straightforward. It looks at a particular set of rows and finds the minimum
value of the column which is provided as an argument to it. For example, in our example table we
wish to find out from which year do we have records of programming languages. Analyzing the
problem at hand, we see that if we apply the aggregate function MIN to the field year in our table,
we should get the desired output.
Listing: finding out the earliest year value in our table
1 SELECT MIN(year) from proglang_tbl ;
2
3 > 1957
The MAX function similarly finds the largest value in the column provided to it as an argument.
Listing: finding out the latest year value in our table and the programming language associated with it
1 select language, MAX(year) year from proglang_tbl ;
language
1988
year
Tel
10.4 Grouping Data
The GROUP BY clause of a SELECT query is used to group records based upon their field values.
This clause is placed after the 'WHERE conditional. For example, in our sample table we can group
Aggregation and Grouping
42
data by which committee decided on their standard.
Listing: Grouping records by its fields
1 SELECT language, standard FROM proglang_tbl
2 WHERE standard IS NOT NULL
3 GROUP BY standard, language;
Figure: output for grouping records
language
standard
APL
ANSI
Fortran
ANSI
PL/I
ECMA
Prolog
ISO
The interesting thing to note here is the rule that the columns listed in the SELECT clause must be
present in the GROUP BY clause. This leads us to the following two corollaries.
1. You cannot group by a column which is not present in the SELECT list.
2. You must specify all the columns in the grouping clause which are present in the SELECT list.
Another useful way to use grouping is to combine the operation with an aggregate function.
Supposing we wish to count how many standards a particular organization has in our table. This
can be achieved by combining the GROUP BY clause with the COUNT aggregate function as given
below.
Listing: using GROUP BY with aggregate functions
1 SELECT standard, count(*) FROM proglang_tbl GROUP BY standard;
Figure: query output showing the count of standard organizations in our table
standard col2
ANSI 2
ECMA 1
ISO 1
(null) 2
Aggregation and Grouping
43
10.5 The HAVING Clause
Like a 'WHERE clause places conditions on the fields of a query, the HAVING clause places conditions
on the groups created by GROUP BY. It must be placed immediately after the GROUP BY but before
the ORDER BY clause.
Listing: demonstration of the HAVING clause
1 SELECT language, standard, year FROM proglang_tbl
2 GROUP BY standard, year, language HAVING year < 1980;
Figure: output of the HAVING clause demonstration query
language
standard
year
APL
ANSI
1964
Fortran
ANSI
1957
PL/I
ECMA
1964
Prolog
ISO
1972
From the output we can clearly see that the records for Perl and Tel are left out since they do not
satisfy the HAVING conditional of being created before 1980.
The output of the previous query demonstrating the GROUP BY and HAVING clause is not
according to the SQL standard. Ingres 10.1 would display the result as above in its default
configuration, but other database management systems adhering to the standard would
swap the Fortran and APL records. This is because in the GROUP BY order first dictates
grouping by standard and then year (1957 < 1964). This illustrates an important point, every
relational database vendor’s implementation differs from the SQL standard in one way or
another.
11. Understandingjoins
11.1 What is a Join?
A join operation allows you to retrieve data from multiple tables in a single SELECT query. Two
tables can be joined by a single join operator, but the result can be joined again with other tables.
There must exist a same or similar column between the tables being joined.
When you design an entire database system using good design principles like normalization , we
often require the use of joins to give a complete picture to a user’s query. For example, we split
our programming languages table into two - one holding the author details and the other holding
information about the languages itself. To show a report listing authors and which programming
language they created, we would have to use a join.
Figure: authors tbl contents
authorid
author
languageid
1
Colmerauer
1
2
Wall
2
3
Ousterhout
4
4
Iverson
3
5
Kemeny
5
6
Kurtz
5
id
language
Figure: newlang_tbl contents
year
standard
1
Prolog
1972
ISO
2
Perl
1987
(null)
3
APL
1964
ANSI
4
Tel
1988
(null)
5
BASIC
1964
ANSI
We now form a query to show our desired output - the list of all authors with the corresponding
language they developed. We choose our join column as the language_id field from the authors
table. This corresponds to the id field in the languages table.
Understanding Joins
45
Listing: running a join operation on our two tables
SELECT author, language FROM
WHERE language_id = id;
authors_tbl, newlang_tbl
author
Figure: result of our join query
language
Colmerauer
Prolog
Wall
Perl
Iverson
APL
Ousterhout
Tel
Kemeny
BASIC
Kurtz
BASIC
The output of our query combines a column from both tables giving us a better report. The
language_id = id is called the join condition . Since the operator used in the join condition is an
equality operator (=), this join is called as an equijoin . Another important thing to note is that the
columns participating in the join condition are not the ones we choose to be in the result of the
query.
11.2 Alternative Join Syntax
You would have noticed that we formed our join query without much special syntax, using our
regular FROM/WHERE combination. The SQL-92 standard introduced the JOIN keyword to allow
us to form join queries. Since it was introduced earlier, the FROM/WHERE syntax is more common.
But now that the majority of database vendors have implemented most of the SQL-92 standard, the
JOIN syntax is also in widespread use. Below is the JOIN syntax equivalent of the query we just
wrote to display which author created which programming language.
Listing: Rewriting our query using the JOIN(SQL-92) syntax
1 SELECT author, language FROM authors_tbl JOIN newlang_tbl
2 ON language_id = id;
Notice that instead separating the two tables using a comma (thereby making it a list), we use
the JOIN keyword. The columns which participate in the join condition are preceded by the ON
keyword. The WHERE clause can then be used after the join condition specification (ON clause) to
specify any further conditions if needed.
Understanding Joins
46
11.3 Resolving ambiguity in join columns
In our example the join condition fields had distinct names - id and language_id. But what if in our
languages table ( newlang_tbl ) we kept the key field’s name as language_id. This would create an
ambiguity in the join condition, which would become the confusing language_id = language_id. To
resolve this, we need to qualify the column by prepending it by the table name it belongs to and a
.(period).
Listing: Resolving the naming ambiguity by qualifying the columns
1 SELECT author, language FROM authors_tbl JOIN newlang_tbl
2 ON authors_tbl . language_id = newlang_tbl . language_id;
Another way to solve such ambiguity is to qualify the columns using table aliases . The concept is
to give a short name to a table and then use this to qualify the columns instead of a long, unwieldy
table name.
Listing: using table aliases
1 SELECT author, language FROM authors_tbl a JOIN newlang_tbl 1
2 ON a . language_id = l.id;
Here the authors table is given the alias a and the languages table is given the alias /. It is generally
considered a good practice to qualify column names of a join condition regardless of whether there
is a name ambiguity or not.
11.4 Cross Joins
You might think what would happen if we left out the join condition from our query. Well what
happens in the background of running a join query is that first all possible combinations of rows
are made from the tables participating in the join. Then the rows which satisfy the join condition
are chosen for the output (or further processing). If we leave out the join condition, we get as the
output all possible combinations of records. This is called a Cross Join** or Cartesian Product of
the tables usually denoted by the sign X.
Listing: query for showing the cartesian product of our tables
1 SELECT author, language FROM authors_tbl, newlang_tbl;
Understanding Joins
47
Figure: the cartesian product of our tables
author language
Kemeny
BASIC
Kurtz
BASIC
Colmerauer
BASIC
Wall
BASIC
Ousterhout
BASIC
Iverson
BASIC
Kemeny
Prolog
Kurtz
Prolog
Colmerauer
Prolog
Wall
Prolog
Ousterhout
Prolog
Iverson
Prolog
Kemeny
Perl
Kurtz
Perl
Colmerauer
Perl
Wall
Perl
Ousterhout
Perl
Another way to rewrite this query is to actually use the JOIN keyword with a preceding argument
CROSS as shown below.
Listing: rewriting the query using CROSS JOIN
SELECT author, language FROM authors_tbl CROSS JOIN newlang_tbl ;
11.5 Self Joins
Sometimes a table within its own columns has meaningful data but one (or more) of its fields refer
to another field in the same table. For example if we have a table in which we capture programming
languages which influenced other programming languages and denote the influence relationship
by the language id, to show the resolved output we would have to join the table with itself. This is
also called a SELF 30IN. Consider the table created below and pay close attention to the data being
inserted.
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
Understanding Joins
48
Listing: creating and populating our language influence table
CREATE TABLE inflang_tbl (id INTEGER PRIMARY KEY,
language VARCHAR(20) NOT NULL,
inf luenced_by INTEGER);
INSERT INTO inflang_tbl (id, language)
VALUES (1, 'Fortran');
INSERT INTO inflang_tbl (id, language, inf luenced_by )
VALUES (2, 'Pascal ’ , 3);
INSERT INTO inflang_tbl (id, language, inf luenced_by )
VALUES (3, 'Algol ' , 1);
Figure: contents of inflang tbl
id language influencedby
1 Fortran (null)
2 Pascal 3
3 Algol 1
Our goal is to now write a self join query to display which language influenced which one, i.e.
resolve the influenced_by column.
Listing: running a self join query
SELECT 11 . language, 12. language AS influenced
FROM inflang_tbl 11, inflang_tbl 12
WHERE 11. id = 12 . inf luenced_by;
Notice the use of table aliases to qualify the join condition columns as separate and the use of the
AS keyword which renames the column in the output.
Figure: result of our self join query
language influenced
Algol
Fortran
Pascal
Algol
12. Subqueries
A subquery, simply put, is a query written as a part of a bigger statement. Think of it as a SELECT
statement inside another one. The result of the inner SELECT can then be used in the outer query.
Let us take a simple example to illustrate this. Consider the same source tables as the ones in the
Understanding Joins chapter - authors_tbl and newlang_tbl. We will try to write a query (and a
subquery) to display the author of a particular language.
Listing: A simple subquery example
1 SELECT author FROM authors_tbl
2 WHERE language_id IN (SELECT id FROM newlang_tbl WHERE language= ' Tel ' ) ;
3
4 > Ousterhout
The subquery SELECT id FROM newlang_tbl WHERE language= 1 Tel ' picks the correct language id
from the newlang_tbl and passes it on to the outer query on the authors table. This frees us from the
responsibility of joining the two tables using the language id field. Which approach to take in certain
situations - a join, a subquery or a combination of both - is mostly a matter of personal preference.
Other times, one approach will be clearly the superior choice.
12.1 Types of subqueries
We can broadly classify subqueries into three categories.
1. Scalar subqueries A subquery that returns only a single column of a single row as its output.
The example in the previous section, where the subquery returns the id for Tel is a scalar
sub query.
2. Table subqueries A table subquery can return more than a single row and many columns
per row. In essence, it can return a table itself to take part in your outer query. Let us take
an example where we wish to display all the programming language writers who created a
language after 1980.
Subqueries
50
Listing: A table subquery example
1 SELECT author, language FROM authors_tbl a, (SELECT id, language FROM newlang_tb\
2 1 WHERE year > 1980) n
WHERE a . language_id = n.id;
Carefully study the FROM clause of the query above. Our table subquery is placed within it and it
returns a set of languages which were created after 1980. The result consists of two rows and two
columns, one of which i.e. language is picked up to be displayed in the final output.
Figure: authors who created their languages after 1980
author language
Wall Perl
Ousterhout Tel
1. Row subqueries A subquery that returns a single row but more than one column is called
a row subquery. These are the least important type of subqueries since most database
management systems do not support it, including SQLite.
12.2 Using subqueries in INSERT statements
We can even use subqueries inside other SQL statement like INSERT. Let us try to add a new language
and a new author in our tables and ease our task of remembering id numbers by just a bit by using
subqueries.
Listing: Inserting a new programming language
1 INSERT INTO newlang_tbl (id, language, year, standard)
2 VALUES (6, 'Pascal', 1970, 'ISO');
The contents of our table now look as shown below.
id
language
year
standard
1
Prolog
1972
ISO
2
Perl
1987
3
APL
1964
ANSI
4
Tel
1988
5
BASIC
1964
ANSI
6
Pascal
1970
ISO
Subqueries
51
While inserting a new entry into the authors_tbl, we can either remember that we used the
language_id as 6 for Pascal, or use a subquery. Let us see an example of the latter approach.
Listing: Inserting a new author using a subquery
1 INSERT INTO authors_tbl (author_id, author, language_id)
2 VALUES (7, 'Wirth', (SELECT id FROM newlang_tbl WHERE language= ' Pascal 1 )) ;
We believe that this should put the correct language id for Wirth since he created Pascal. Let us
verify this belief by looking at te contents of the table.
authorid
author
languageid
5
Kemeny
5
6
Kurtz
5
1
Colmerauer
1
2
Wall
2
3
Ousterhout
4
4
Iverson
3
7
Wirth
6
Further Reading
1. Sams Teach Yourself SQL in 10 Minutes (4th Edition, 2012) by Ben Forta
2. The Language of SQL: How to Access Data in Relational Databases (1st Edition, 2010) by Larry
Rockoff
3. The Practical SQL Handbook: Using SQL Variants (4th Edition, 2001) by Judith S Bowman,
Sandra L Emerson, Marcy Darnovsky
4. Sams Teach Yourself SQL in 24 Hours (5th Edition, 2011) by Ryan Stephens, Ron Plew, Arie D
Jones
5. Introduction to SQL: Mastering the Relational Database Language (4th Edition, 2006) by Rick
F van derLans
Appendix: Major Database
Management Systems
1. Ingres ( Actian Corporation)
A full featured relational database management system available as a proprietary or an open
source edition.
http://www.actian.com/products/ingres
2. Oracle Database ( Oracle Corporation )
An enterprise level database management system with a free to use Express Edition.
http://www.oracle.com/technetwork/products/express-edition/overview/index.html
3. IBM DB2 ( IBM Corporation)
A powerful relational database management system with a free edition called DB2 Express-C.
http://www-01.ibm.com/software/data/db2/express/
4. PostgreSQL
Open Source relational database management system with tons of features.
http://www.postgresql.org/
5. MySQL ( Oracle Corporation)
Popular and easy to use open source DBMS.
http://www.mysql.com/
6. Firebird
Full featured, open source relational DBMS.
http://www.firebirdsql.org/
7. SQLite (D. Richard Hipp)
Popular, small and free to use embeddable database system.
http://sqlite.org/
8. Access ( Microsoft Corporation)
Personal relational database system with a graphical interface.
http://office.microsoft.com/access
9. SQL Server ( Microsoft Corporation)
Proprietary, powerful dbms with a free to use express edition.
https://www.microsoft.com/en-us/sqlserver/editions/2012-editions/express.aspx
Glossary
Alias
Cross Join
Database
DBMS
Field
Foreign Key
Normalization
Record
SQL
Table
A temporary name given to a table in the FROM clause
A join listing all possible combination of rows without filtering
A collection of organized data. Can be stored in a digital format
like on a computer
Database Management System. A software to control and manage
digital databases
A column in a table
A column in a table that matches a primary key column in another
table
Breaking down a raw database into tables and removing
redundancies
A row of a table
Structured Query Language. A language used to interact with
databases
A matrix like display/abstraction of data in row-column format
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Console Living Room
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Understanding
9/11